Add evaluation results on the amazon_polarity config and test split of amazon_polarity (#2)

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	---
	license: apache-2.0
	tags:
	- generated_from_trainer
	- sibyl
	datasets:
	- amazon_polarity
	metrics:
	- accuracy
	model-index:
	- name: bert-base-uncased-amazon_polarity
	results:
	- task:
	name: Text Classification
	type: text-classification
	dataset:
	name: amazon_polarity
	type: amazon_polarity
	args: amazon_polarity
	metrics:
	- name: Accuracy
	type: accuracy
	value: 0.94647
	- task:
	type: text-classification
	name: Text Classification
	dataset:
	name: amazon_polarity
	type: amazon_polarity
	config: amazon_polarity
	split: test
	metrics:
	- name: Accuracy
	type: accuracy
	value: 0.9464875
	verified: true
	- name: Precision
	type: precision
	value: 0.9528844934702675
	verified: true
	- name: Recall
	type: recall
	value: 0.939425
	verified: true
	- name: AUC
	type: auc
	value: 0.9863499156250001
	verified: true
	- name: F1
	type: f1
	value: 0.9461068798388619
	verified: true
	- name: loss
	type: loss
	value: 0.2944573760032654
	verified: true
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# bert-base-uncased-amazon_polarity

	This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the amazon_polarity dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.2945
	- Accuracy: 0.9465

	## Model description

	More information needed

	## Intended uses & limitations

	More information needed

	## Training and evaluation data

	More information needed

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 5e-05
	- train_batch_size: 1
	- eval_batch_size: 8
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_steps: 1782000
	- training_steps: 17820000

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \|
	\|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\|
	\| 0.7155 \| 0.0 \| 2000 \| 0.7060 \| 0.4622 \|
	\| 0.7054 \| 0.0 \| 4000 \| 0.6925 \| 0.5165 \|
	\| 0.6842 \| 0.0 \| 6000 \| 0.6653 \| 0.6116 \|
	\| 0.6375 \| 0.0 \| 8000 \| 0.5721 \| 0.7909 \|
	\| 0.4671 \| 0.0 \| 10000 \| 0.3238 \| 0.8770 \|
	\| 0.3403 \| 0.0 \| 12000 \| 0.3692 \| 0.8861 \|
	\| 0.4162 \| 0.0 \| 14000 \| 0.4560 \| 0.8908 \|
	\| 0.4728 \| 0.0 \| 16000 \| 0.5071 \| 0.8980 \|
	\| 0.5111 \| 0.01 \| 18000 \| 0.5204 \| 0.9015 \|
	\| 0.4792 \| 0.01 \| 20000 \| 0.5193 \| 0.9076 \|
	\| 0.544 \| 0.01 \| 22000 \| 0.4835 \| 0.9133 \|
	\| 0.4745 \| 0.01 \| 24000 \| 0.4689 \| 0.9170 \|
	\| 0.4403 \| 0.01 \| 26000 \| 0.4778 \| 0.9177 \|
	\| 0.4405 \| 0.01 \| 28000 \| 0.4754 \| 0.9163 \|
	\| 0.4375 \| 0.01 \| 30000 \| 0.4808 \| 0.9175 \|
	\| 0.4628 \| 0.01 \| 32000 \| 0.4340 \| 0.9244 \|
	\| 0.4488 \| 0.01 \| 34000 \| 0.4162 \| 0.9265 \|
	\| 0.4608 \| 0.01 \| 36000 \| 0.4031 \| 0.9271 \|
	\| 0.4478 \| 0.01 \| 38000 \| 0.4502 \| 0.9253 \|
	\| 0.4237 \| 0.01 \| 40000 \| 0.4087 \| 0.9279 \|
	\| 0.4601 \| 0.01 \| 42000 \| 0.4133 \| 0.9269 \|
	\| 0.4153 \| 0.01 \| 44000 \| 0.4230 \| 0.9306 \|
	\| 0.4096 \| 0.01 \| 46000 \| 0.4108 \| 0.9301 \|
	\| 0.4348 \| 0.01 \| 48000 \| 0.4138 \| 0.9309 \|
	\| 0.3787 \| 0.01 \| 50000 \| 0.4066 \| 0.9324 \|
	\| 0.4172 \| 0.01 \| 52000 \| 0.4812 \| 0.9206 \|
	\| 0.3897 \| 0.02 \| 54000 \| 0.4013 \| 0.9325 \|
	\| 0.3787 \| 0.02 \| 56000 \| 0.3837 \| 0.9344 \|
	\| 0.4253 \| 0.02 \| 58000 \| 0.3925 \| 0.9347 \|
	\| 0.3959 \| 0.02 \| 60000 \| 0.3907 \| 0.9353 \|
	\| 0.4402 \| 0.02 \| 62000 \| 0.3708 \| 0.9341 \|
	\| 0.4115 \| 0.02 \| 64000 \| 0.3477 \| 0.9361 \|
	\| 0.3876 \| 0.02 \| 66000 \| 0.3634 \| 0.9373 \|
	\| 0.4286 \| 0.02 \| 68000 \| 0.3778 \| 0.9378 \|
	\| 0.422 \| 0.02 \| 70000 \| 0.3540 \| 0.9361 \|
	\| 0.3732 \| 0.02 \| 72000 \| 0.3853 \| 0.9378 \|
	\| 0.3641 \| 0.02 \| 74000 \| 0.3951 \| 0.9386 \|
	\| 0.3701 \| 0.02 \| 76000 \| 0.3582 \| 0.9388 \|
	\| 0.4498 \| 0.02 \| 78000 \| 0.3268 \| 0.9375 \|
	\| 0.3587 \| 0.02 \| 80000 \| 0.3825 \| 0.9401 \|
	\| 0.4474 \| 0.02 \| 82000 \| 0.3155 \| 0.9391 \|
	\| 0.3598 \| 0.02 \| 84000 \| 0.3666 \| 0.9388 \|
	\| 0.389 \| 0.02 \| 86000 \| 0.3745 \| 0.9377 \|
	\| 0.3625 \| 0.02 \| 88000 \| 0.3776 \| 0.9387 \|
	\| 0.3511 \| 0.03 \| 90000 \| 0.4275 \| 0.9336 \|
	\| 0.3428 \| 0.03 \| 92000 \| 0.4301 \| 0.9336 \|
	\| 0.4042 \| 0.03 \| 94000 \| 0.3547 \| 0.9359 \|
	\| 0.3583 \| 0.03 \| 96000 \| 0.3763 \| 0.9396 \|
	\| 0.3887 \| 0.03 \| 98000 \| 0.3213 \| 0.9412 \|
	\| 0.3915 \| 0.03 \| 100000 \| 0.3557 \| 0.9409 \|
	\| 0.3378 \| 0.03 \| 102000 \| 0.3627 \| 0.9418 \|
	\| 0.349 \| 0.03 \| 104000 \| 0.3614 \| 0.9402 \|
	\| 0.3596 \| 0.03 \| 106000 \| 0.3834 \| 0.9381 \|
	\| 0.3519 \| 0.03 \| 108000 \| 0.3560 \| 0.9421 \|
	\| 0.3598 \| 0.03 \| 110000 \| 0.3485 \| 0.9419 \|
	\| 0.3642 \| 0.03 \| 112000 \| 0.3754 \| 0.9395 \|
	\| 0.3477 \| 0.03 \| 114000 \| 0.3634 \| 0.9426 \|
	\| 0.4202 \| 0.03 \| 116000 \| 0.3071 \| 0.9427 \|
	\| 0.3656 \| 0.03 \| 118000 \| 0.3155 \| 0.9441 \|
	\| 0.3709 \| 0.03 \| 120000 \| 0.2923 \| 0.9433 \|
	\| 0.374 \| 0.03 \| 122000 \| 0.3272 \| 0.9441 \|
	\| 0.3142 \| 0.03 \| 124000 \| 0.3348 \| 0.9444 \|
	\| 0.3452 \| 0.04 \| 126000 \| 0.3603 \| 0.9436 \|
	\| 0.3365 \| 0.04 \| 128000 \| 0.3339 \| 0.9434 \|
	\| 0.3353 \| 0.04 \| 130000 \| 0.3471 \| 0.9450 \|
	\| 0.343 \| 0.04 \| 132000 \| 0.3508 \| 0.9418 \|
	\| 0.3174 \| 0.04 \| 134000 \| 0.3753 \| 0.9436 \|
	\| 0.3009 \| 0.04 \| 136000 \| 0.3687 \| 0.9422 \|
	\| 0.3785 \| 0.04 \| 138000 \| 0.3818 \| 0.9396 \|
	\| 0.3199 \| 0.04 \| 140000 \| 0.3291 \| 0.9438 \|
	\| 0.4049 \| 0.04 \| 142000 \| 0.3372 \| 0.9454 \|
	\| 0.3435 \| 0.04 \| 144000 \| 0.3315 \| 0.9459 \|
	\| 0.3814 \| 0.04 \| 146000 \| 0.3462 \| 0.9401 \|
	\| 0.359 \| 0.04 \| 148000 \| 0.3981 \| 0.9361 \|
	\| 0.3552 \| 0.04 \| 150000 \| 0.3226 \| 0.9469 \|
	\| 0.345 \| 0.04 \| 152000 \| 0.3731 \| 0.9384 \|
	\| 0.3228 \| 0.04 \| 154000 \| 0.2956 \| 0.9471 \|
	\| 0.3637 \| 0.04 \| 156000 \| 0.2869 \| 0.9477 \|
	\| 0.349 \| 0.04 \| 158000 \| 0.3331 \| 0.9430 \|
	\| 0.3374 \| 0.04 \| 160000 \| 0.4159 \| 0.9340 \|
	\| 0.3718 \| 0.05 \| 162000 \| 0.3241 \| 0.9459 \|
	\| 0.315 \| 0.05 \| 164000 \| 0.3544 \| 0.9391 \|
	\| 0.3215 \| 0.05 \| 166000 \| 0.3311 \| 0.9451 \|
	\| 0.3464 \| 0.05 \| 168000 \| 0.3682 \| 0.9453 \|
	\| 0.3495 \| 0.05 \| 170000 \| 0.3193 \| 0.9469 \|
	\| 0.305 \| 0.05 \| 172000 \| 0.4132 \| 0.9389 \|
	\| 0.3479 \| 0.05 \| 174000 \| 0.3465 \| 0.947 \|
	\| 0.3537 \| 0.05 \| 176000 \| 0.3277 \| 0.9449 \|


	### Framework versions

	- Transformers 4.10.2
	- Pytorch 1.7.1
	- Datasets 1.12.1
	- Tokenizers 0.10.3

	---
	license: apache-2.0
	tags:
	- generated_from_trainer
	- sibyl
	datasets:
	- amazon_polarity
	metrics:
	- accuracy
	model-index:
	- name: bert-base-uncased-amazon_polarity
	results:
	- task:
	name: Text Classification
	type: text-classification
	dataset:
	name: amazon_polarity
	type: amazon_polarity
	args: amazon_polarity
	metrics:
	- name: Accuracy
	type: accuracy
	value: 0.94647
	- task:
	type: text-classification
	name: Text Classification
	dataset:
	name: amazon_polarity
	type: amazon_polarity
	config: amazon_polarity
	split: test
	metrics:
	- name: Accuracy
	type: accuracy
	value: 0.9464875
	verified: true
	- name: Precision
	type: precision
	value: 0.9528844934702675
	verified: true
	- name: Recall
	type: recall
	value: 0.939425
	verified: true
	- name: AUC
	type: auc
	value: 0.9863499156250001
	verified: true
	- name: F1
	type: f1
	value: 0.9461068798388619
	verified: true
	- name: loss
	type: loss
	value: 0.2944573760032654
	verified: true
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# bert-base-uncased-amazon_polarity

	This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the amazon_polarity dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.2945
	- Accuracy: 0.9465

	## Model description

	More information needed

	## Intended uses & limitations

	More information needed

	## Training and evaluation data

	More information needed

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 5e-05
	- train_batch_size: 1
	- eval_batch_size: 8
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_steps: 1782000
	- training_steps: 17820000

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \|
	\|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\|
	\| 0.7155 \| 0.0 \| 2000 \| 0.7060 \| 0.4622 \|
	\| 0.7054 \| 0.0 \| 4000 \| 0.6925 \| 0.5165 \|
	\| 0.6842 \| 0.0 \| 6000 \| 0.6653 \| 0.6116 \|
	\| 0.6375 \| 0.0 \| 8000 \| 0.5721 \| 0.7909 \|
	\| 0.4671 \| 0.0 \| 10000 \| 0.3238 \| 0.8770 \|
	\| 0.3403 \| 0.0 \| 12000 \| 0.3692 \| 0.8861 \|
	\| 0.4162 \| 0.0 \| 14000 \| 0.4560 \| 0.8908 \|
	\| 0.4728 \| 0.0 \| 16000 \| 0.5071 \| 0.8980 \|
	\| 0.5111 \| 0.01 \| 18000 \| 0.5204 \| 0.9015 \|
	\| 0.4792 \| 0.01 \| 20000 \| 0.5193 \| 0.9076 \|
	\| 0.544 \| 0.01 \| 22000 \| 0.4835 \| 0.9133 \|
	\| 0.4745 \| 0.01 \| 24000 \| 0.4689 \| 0.9170 \|
	\| 0.4403 \| 0.01 \| 26000 \| 0.4778 \| 0.9177 \|
	\| 0.4405 \| 0.01 \| 28000 \| 0.4754 \| 0.9163 \|
	\| 0.4375 \| 0.01 \| 30000 \| 0.4808 \| 0.9175 \|
	\| 0.4628 \| 0.01 \| 32000 \| 0.4340 \| 0.9244 \|
	\| 0.4488 \| 0.01 \| 34000 \| 0.4162 \| 0.9265 \|
	\| 0.4608 \| 0.01 \| 36000 \| 0.4031 \| 0.9271 \|
	\| 0.4478 \| 0.01 \| 38000 \| 0.4502 \| 0.9253 \|
	\| 0.4237 \| 0.01 \| 40000 \| 0.4087 \| 0.9279 \|
	\| 0.4601 \| 0.01 \| 42000 \| 0.4133 \| 0.9269 \|
	\| 0.4153 \| 0.01 \| 44000 \| 0.4230 \| 0.9306 \|
	\| 0.4096 \| 0.01 \| 46000 \| 0.4108 \| 0.9301 \|
	\| 0.4348 \| 0.01 \| 48000 \| 0.4138 \| 0.9309 \|
	\| 0.3787 \| 0.01 \| 50000 \| 0.4066 \| 0.9324 \|
	\| 0.4172 \| 0.01 \| 52000 \| 0.4812 \| 0.9206 \|
	\| 0.3897 \| 0.02 \| 54000 \| 0.4013 \| 0.9325 \|
	\| 0.3787 \| 0.02 \| 56000 \| 0.3837 \| 0.9344 \|
	\| 0.4253 \| 0.02 \| 58000 \| 0.3925 \| 0.9347 \|
	\| 0.3959 \| 0.02 \| 60000 \| 0.3907 \| 0.9353 \|
	\| 0.4402 \| 0.02 \| 62000 \| 0.3708 \| 0.9341 \|
	\| 0.4115 \| 0.02 \| 64000 \| 0.3477 \| 0.9361 \|
	\| 0.3876 \| 0.02 \| 66000 \| 0.3634 \| 0.9373 \|
	\| 0.4286 \| 0.02 \| 68000 \| 0.3778 \| 0.9378 \|
	\| 0.422 \| 0.02 \| 70000 \| 0.3540 \| 0.9361 \|
	\| 0.3732 \| 0.02 \| 72000 \| 0.3853 \| 0.9378 \|
	\| 0.3641 \| 0.02 \| 74000 \| 0.3951 \| 0.9386 \|
	\| 0.3701 \| 0.02 \| 76000 \| 0.3582 \| 0.9388 \|
	\| 0.4498 \| 0.02 \| 78000 \| 0.3268 \| 0.9375 \|
	\| 0.3587 \| 0.02 \| 80000 \| 0.3825 \| 0.9401 \|
	\| 0.4474 \| 0.02 \| 82000 \| 0.3155 \| 0.9391 \|
	\| 0.3598 \| 0.02 \| 84000 \| 0.3666 \| 0.9388 \|
	\| 0.389 \| 0.02 \| 86000 \| 0.3745 \| 0.9377 \|
	\| 0.3625 \| 0.02 \| 88000 \| 0.3776 \| 0.9387 \|
	\| 0.3511 \| 0.03 \| 90000 \| 0.4275 \| 0.9336 \|
	\| 0.3428 \| 0.03 \| 92000 \| 0.4301 \| 0.9336 \|
	\| 0.4042 \| 0.03 \| 94000 \| 0.3547 \| 0.9359 \|
	\| 0.3583 \| 0.03 \| 96000 \| 0.3763 \| 0.9396 \|
	\| 0.3887 \| 0.03 \| 98000 \| 0.3213 \| 0.9412 \|
	\| 0.3915 \| 0.03 \| 100000 \| 0.3557 \| 0.9409 \|
	\| 0.3378 \| 0.03 \| 102000 \| 0.3627 \| 0.9418 \|
	\| 0.349 \| 0.03 \| 104000 \| 0.3614 \| 0.9402 \|
	\| 0.3596 \| 0.03 \| 106000 \| 0.3834 \| 0.9381 \|
	\| 0.3519 \| 0.03 \| 108000 \| 0.3560 \| 0.9421 \|
	\| 0.3598 \| 0.03 \| 110000 \| 0.3485 \| 0.9419 \|
	\| 0.3642 \| 0.03 \| 112000 \| 0.3754 \| 0.9395 \|
	\| 0.3477 \| 0.03 \| 114000 \| 0.3634 \| 0.9426 \|
	\| 0.4202 \| 0.03 \| 116000 \| 0.3071 \| 0.9427 \|
	\| 0.3656 \| 0.03 \| 118000 \| 0.3155 \| 0.9441 \|
	\| 0.3709 \| 0.03 \| 120000 \| 0.2923 \| 0.9433 \|
	\| 0.374 \| 0.03 \| 122000 \| 0.3272 \| 0.9441 \|
	\| 0.3142 \| 0.03 \| 124000 \| 0.3348 \| 0.9444 \|
	\| 0.3452 \| 0.04 \| 126000 \| 0.3603 \| 0.9436 \|
	\| 0.3365 \| 0.04 \| 128000 \| 0.3339 \| 0.9434 \|
	\| 0.3353 \| 0.04 \| 130000 \| 0.3471 \| 0.9450 \|
	\| 0.343 \| 0.04 \| 132000 \| 0.3508 \| 0.9418 \|
	\| 0.3174 \| 0.04 \| 134000 \| 0.3753 \| 0.9436 \|
	\| 0.3009 \| 0.04 \| 136000 \| 0.3687 \| 0.9422 \|
	\| 0.3785 \| 0.04 \| 138000 \| 0.3818 \| 0.9396 \|
	\| 0.3199 \| 0.04 \| 140000 \| 0.3291 \| 0.9438 \|
	\| 0.4049 \| 0.04 \| 142000 \| 0.3372 \| 0.9454 \|
	\| 0.3435 \| 0.04 \| 144000 \| 0.3315 \| 0.9459 \|
	\| 0.3814 \| 0.04 \| 146000 \| 0.3462 \| 0.9401 \|
	\| 0.359 \| 0.04 \| 148000 \| 0.3981 \| 0.9361 \|
	\| 0.3552 \| 0.04 \| 150000 \| 0.3226 \| 0.9469 \|
	\| 0.345 \| 0.04 \| 152000 \| 0.3731 \| 0.9384 \|
	\| 0.3228 \| 0.04 \| 154000 \| 0.2956 \| 0.9471 \|
	\| 0.3637 \| 0.04 \| 156000 \| 0.2869 \| 0.9477 \|
	\| 0.349 \| 0.04 \| 158000 \| 0.3331 \| 0.9430 \|
	\| 0.3374 \| 0.04 \| 160000 \| 0.4159 \| 0.9340 \|
	\| 0.3718 \| 0.05 \| 162000 \| 0.3241 \| 0.9459 \|
	\| 0.315 \| 0.05 \| 164000 \| 0.3544 \| 0.9391 \|
	\| 0.3215 \| 0.05 \| 166000 \| 0.3311 \| 0.9451 \|
	\| 0.3464 \| 0.05 \| 168000 \| 0.3682 \| 0.9453 \|
	\| 0.3495 \| 0.05 \| 170000 \| 0.3193 \| 0.9469 \|
	\| 0.305 \| 0.05 \| 172000 \| 0.4132 \| 0.9389 \|
	\| 0.3479 \| 0.05 \| 174000 \| 0.3465 \| 0.947 \|
	\| 0.3537 \| 0.05 \| 176000 \| 0.3277 \| 0.9449 \|


	### Framework versions

	- Transformers 4.10.2
	- Pytorch 1.7.1
	- Datasets 1.12.1
	- Tokenizers 0.10.3